“Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us” Invited Talk Ayasdi Menlo Park, CA December 5, 2014 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD 1 http://lsmarr.calit2.net
From One to a Billion Data Points Defining Me: The Exponential Rise in Body Data in Just One Decade Billion: My F Mic ull rob DN ial A, Genome MRI/CT Images Improving Body SNPs Million: My DNA SNPs, Zeo, FitBit Discovering Disease Blood Variables One: Hundred: My Blood Variables My Weight Weight
How Will Detailed Knowledge of Microbiome Ecology Radically Change Medicine and Wellness? Your Body Has 10 Times As Many Microbe Cells As Human Cells 99% of Your DNA Genes Are in Microbe Cells Not Human Cells Challenge: Map Out Microbial Ecology and Function in Health and Disease States
Intense Scientific Research is Underway on Understanding the Human Microbiome June 8, 2012 June 14, 2012 August 18, 2012
To Map Out the Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers Illumina HiSeq 2000 at JCVI Example: Inflammatory Bowel Disease (IBD) • Metagenomic Sequencing – JCVI Produced – ~150 Billion DNA Bases From Seven of LS Stool Samples Over 1.5 Years – We Downloaded ~3 Trillion DNA Bases SDSC Gordon Data Supercomputer From NIH Human Microbiome Program Data Base – 255 Healthy People, 21 with IBD • Supercomputing (Weizhong Li, JCVI/HLI/UCSD): – ~20 CPU-Years on SDSC’s Gordon – ~4 CPU-Years on Dell’s HPC Cloud • Produced Relative Abundance of – ~10,000 Bacteria, Archaea, Viruses in ~300 People – ~3Million Filled Spreadsheet Cells
Computational NextGen Sequencing Pipeline: From Sequence to Taxonomy and Function PI: (Weizhong Li, CRBS, UCSD): NIH R01HG005978 (2010-2013, $1.1M)
Next Step Programmability, Scalability and Reproducibility using bioKepler www.biokepler.org Optimized Source: Local Cluster Cloud National Ilkay Resources Resources Resources Altintas, SDSC (Gordon) (Comet) (Stampede) (Lonestar) www.kepler-project.org
How Best to Analyze The Microbiome Datasets to Discover Patterns in Health and Disease? Can We Find New Noninvasive Diagnostics In Microbiome Ecologies?
We Found Major State Shifts in Microbial Ecology Phyla Between Healthy and Two Forms of IBD Average HE Most Common Microbial Phyla Average Ulcerative Colitis Average LS Average Crohn’s Disease Explosion of Hybrid of UC and CD Collapse of Bacteroidetes Proteobacteria High Level of Archaea Explosion of Actinobacteria
Using Scalable Visualization Allows Comparison of the Relative Abundance of 200 Microbe Species Comparing 3 LS Time Snapshots (Left) with Healthy, Crohn’s, Ulcerative Colitis (Right Top to Bottom) Calit2 VROOM-FuturePatient Expedition
Using Dell HPC Cloud and Dell Analytics to Discover Microbial Diagnostics for Disease Dynamics • Can We Distinguish Noninvasively Between Health and Disease States? • Are There Subsets of Health or Disease States? • Can We Track Time Development of the Disease State? • Can Novel Microbial Diagnostics Differentiate Health and Disease States?
Using Microbiome Profiles to Survey 155 Subjects for Unhealthy Candidates
Dell Analytics Separates The 4 Patient Types in Our Data Using Our Microbiome Species Data Ulcerative Colitis Colonic Crohn’s Healthy Ileal Crohn’s Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software
I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software
I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s Seven Time Samples Over 1.5 Years Healthy Colonic Crohn’s Ileal Crohn’s
Dell Analytics Tree Graphs Classifies the 4 Health/Disease States With Just 3 Microbe Species Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software
Our Relative Abundance Results Across ~300 People Show Why Dell Analytics Tree Classifier Works UC 100x Healthy Healthy 100x CD LS 100x UC We Produced Similar Results for ~2500 Microbial Species
Using Ayasdi’s Advanced Topological Data Analysis to Separate Healthy from Disease States Using Ayasdi Categorical Data Lens All Healthy All Ileal Crohn’s All Healthy All Healthy Healthy, Ulcerative Colitis, and LS Analysis by Mehrdad Yazdani, Calit2 Talk to Ayasdi in the Intel Booth at SC14
Ayasdi Enables Discovery of Differences Between Healthy and Disease States Using Microbiome Species Healthy LS High in Healthy and LS High in Healthy and Ulcerative Colitis Ileal Crohn’s Ulcerative Colitis High in Both LS and Ileal Crohn’s Disease Using Multidimensional Scaling Lens with Correlation Metric Analysis by Mehrdad Yazdani, Calit2
From Taxonomy to Function: Analysis of LS Clusters of Orthologous Groups (COGs) Analysis: Weizhong Li & Sitao Wu, UCSD
In a “Healthy” Gut Microbiome: Large Taxonomy Variation, Low Protein Family Variation Over 200 People Source: Nature, 486, 207-212 (2012)
Ratio of HE11529 to Ave HE Test to see How Much Variation There is Within Healthy Ratio of Random HE11529 to Healthy Average for Each Nonzero KEGG Most KEGGs Are Within 10x Of Healthy for a Random HE
However, Our Research Shows Large Changes in Protein Families Between Health and Disease Ratio of CD Average to Healthy Average for Each Nonzero KEGG KEGGs Greatly Increased Note Hi/Low In the Disease State Symmetry Most KEGGs Are Within 10x In Healthy and Ileal Crohn’s Disease KEGGs Greatly Decreased In the Disease State Over 7000 KEGGs Which Are Nonzero in Health and Disease States
Note UC Has Many Few KEGGs that are Much Smaller than HE; Also Fewer KEGGs That are Nonzero; Note Asymmetry Between High & Low Ratio of UC Average to Healthy Average for Each Nonzero KEGG KEGGs Greatly Increased In the Disease State Most KEGGs Are Within 10x In Healthy and Ulcerative Colitis KEGGs Greatly Decreased In the Disease State
Note LS001 Has Many Few KEGGs that are Much Smaller than HE; ~Same # KEGGs That are Nonzero; Note Asymmetry Between High & Low Ratio of LS001 Average to Healthy Average for Each Nonzero KEGG KEGGs Greatly Increased In the Disease State Most KEGGs Are Within 10x In Healthy and LS001 KEGGs Greatly Decreased In the Disease State
We Can Define a Subgroup of the 10,000 KEGGs Which Are Extreme in the Disease State • Look for KEGGs That Have the Properties: – Are 100x in All Four Disease States – LS001/Ave HE – Ave CD/ Ave HE – Ave UC/Ave HE – Sick HE Person/Ave HE • There are 48 of These Extreme KEGGs • A New Way to Define What is Wrong with the Microbiome in Disease? • Can We Devise an Ayasdi Lens That Can Separates These Extreme KEGGs?
Using Ayasdi Interactively to Explore Protein Families in Healthy and Disease States Dataset from Larry Smarr Team With 60 Subjects (HE, CD, UC, LS) Each with 10,000 KEGGs - 600,000 Cells Source: Pek Lum, Formerly Chief Data Scientist, Ayasdi
CD is Missing a Population of Bacteria That Exists in High Quantities in HE ( Circled with Arrow) Low in CD and LS • Problem is That These KEGGs Have Moderate Values of Ave CD/ Ave HE • How Can We Change the Ayasdi Lenses So That We Pick Out The Very High Values of Ratios to Ave HE?
Source: Pek Lum, Formerly Chief Data Scientist, Ayasdi
This Ayasdi Lens Does Identify KEGGs In Which Ave CD and LS001 Are Less Than Ave HE • Problem is That These KEGGs Have Moderate Low Values of Ave CD/ Ave HE • How Can We Change the Ayasdi Lenses So That We Pick Out The Very High Values of Ratios to Ave HE?
We Found a Set of Lenes That Clearer Find the 43 Extreme KEGGs K00108(choline_dehydrogenase) K00673(arginine_N-succinyltransferase) K00867(type_I_pantothenate_kinase) K01169(ribonuclease_I_(enterobacter_ribonuclease)) K01484(succinylarginine_dihydrolase) K01682(aconitate_hydratase_2) L-Infinity Centrality Lens K01690(phosphogluconate_dehydratase) K01825(3-hydroxyacyl-CoA_dehydrogenase_/_enoyl-CoA_hydratase_/3-hydroxybutyryl-CoA_epimerase_/_enoyl-CoA_isomerase_[EC:188.8.131.52184.108.40.206_220.127.116.11_18.104.22.168]) Using Norm Correlation K02173(hypothetical_protein) K02317(DNA_replication_protein_DnaT) K02466(glucitol_operon_activator_protein) as Metric K02846(N-methyl-L-tryptophan_oxidase) K03081(3-dehydro-L-gulonate-6-phosphate_decarboxylase) (Resolution: 242, Gain: 5.7) K03119(taurine_dioxygenase) K03181(chorismate--pyruvate_lyase) K03807(AmpE_protein) K05522(endonuclease_VIII) K05775(maltose_operon_periplasmic_protein) K05812(conserved_hypothetical_protein) K05997(Fe-S_cluster_assembly_protein_SufA) K06073(vitamin_B12_transport_system_permease_protein) K06205(MioC_protein) K06445(acyl-CoA_dehydrogenase) K06447(succinylglutamic_semialdehyde_dehydrogenase) K07229(TrkA_domain_protein) K07232(cation_transport_protein_ChaC) K07312(putative_dimethyl_sulfoxide_reductase_subunit_YnfH_(DMSO_reductaseanchor_subunit)) K07336(PKHD-type_hydroxylase) K08989(putative_membrane_protein) K09018(putative_monooxygenase_RutA) K09456(putative_acyl-CoA_dehydrogenase) K09998(arginine_transport_system_permease_protein) K10748(DNA_replication_terminus_site-binding_protein) Entropy & Variance Lens K11209(GST-like_protein) K11391(ribosomal_RNA_large_subunit_methyltransferase_G) Using Angle as Metric K11734(aromatic_amino_acid_transport_protein_AroP) K11735(GABA_permease) (Resolution: 30, Gain 3.00) K11925(SgrR_family_transcriptional_regulator) K12288(pilus_assembly_protein_HofM) K13255(ferric_iron_reductase_protein_FhuF) K14588() K15733() K15834() Analysis by Mehrdad Yazdani, Calit2
Disease Arises from Perturbed Protein Family Networks: Dynamics of a Prion Perturbed Network in Mice Source: Lee Hood, ISB 31 Our Next Goal is to Create Such Perturbed Networks in Humans
Visualizing Time Series of 150 LS Blood and Stool Variables, Each Over 5-10 Years Calit2 64 megapixel VROOM One Blood Draw For Me
Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation 27x Upper Limit Episodic Peaks in Inflammation Followed by Spontaneous Drops Normal Range <1 mg/L Normal Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation
Adding Stool Tests Revealed Oscillatory Behavior in an Immune Variable Typical 124x Upper Limit Lactoferrin Value for Hypothesis: Lactoferrin Oscillations Active Coupled to Relative Abundance IBD of Microbes that Require Iron Normal Range <7.3 µg/mL Antibiotics Antibiotics Lactoferrin is a Protein Shed from Neutrophils - An Antibacterial that Sequesters Iron
Fine Time-Resolution Sampling Enables Analysis of Dynamical Innate and Adaptive Immune Dysfunction Adaptive Immune System Normal Innate Immune System Normal
By Overlaying a Number of Immune/Inflammation Variables, It Appears There May be Phase Correlations CRP SED Lact Lyzo SigA Calp Data Analytics by Benjamin Smarr, UC Berkeley
One Can Use Sine Fitting with Least Squares To Try and Approximate the Time Series Dynamics 5 Sines Data Analytics by Benjamin Smarr, UC Berkeley
With Low Resolution Sine Fitting, There Is Indication of Phase Correlation 2 Sines Data Analytics by Benjamin Smarr, UC Berkeley
Are There Ayasdi Tools to More Deeply Analyze Such Time Series?
UC San Diego Will Be Carrying Out a Major Clinical Study of IBD Using These Techniques Announced Last Friday! Inflammatory Bowel Disease Biobank For Healthy and Disease Patients Already 120 Enrolled, Goal is 1500 Drs. William J. Sandborn, John Chang, & Brigid Boland UCSD School of Medicine, Division of Gastroenterology
Inexpensive Consumer Time Series of Microbiome Now Possible Through Ubiome Data source: LS (Stool Samples); Sequencing and Analysis Ubiome
By Crowdsourcing, Ubiome Can Show I Have a Major Disruption of My Gut Microbiome LS Sample on September 24, 2014 (-) (+) Visit Ubiome in the Exponential Medicine Healthcare Innovation Lab
Where I Believe We are Headed: Predictive, Personalized, Preventive, & Participatory Medicine Will Grow to 1000, Then 10,000, Then 100,000 www.newsweek.com/2009/06/26/a-doctor-s-vision-of-the-future-of-medicine.html
Genetic Sequencing of Humans and Their Microbes Is a Huge Growth Area and the Future Foundation of Medicine Source: @EricTopol Twitter 9/27/2014
Thanks to Our Great Team! UCSD Metagenomics Team JCVI Team Weizhong Li Karen Nelson Sitao Wu Shibu Yooseph Manolito Torralba Calit2@UCSD Future Patient Team SDSC Team Jerry Sheehan Michael Norman Tom DeFanti Mahidhar Tatineni Kevin Patrick Robert Sinkovits Jurgen Schulze Andrew Prudhomme Dell/R Systems Philip Weber Brian Kucic Fred Raab John Thompson Joe Keefe Ernesto Ramirez UCSD Health Sciences Team Ayasdi William J. Sandborn Devi Ramanan Elisabeth Evans Pek Lum John Chang Brigid Boland David Brenner